The program will bring together machine learning experts, statisticians, clinicians, and public health experts to discuss how to harness modern mathematical and computational techniques to better understand health-related data across multiple domains. Basics of various machine learning techniques, including logistic regression, tree-based methods, support vector machines, Bayesian methods, and deep networks will be covered with examples of their applicability in biomedicine and health. Applications will include predicting outcomes for individual patients from clinical and lifestyle parameters, analysing patient data such as X-rays, ultrasound images and ECG measurements, genomic variant analysis, and inferring patterns in heterogeneous large-scale data. Speakers from both computational/statistical and clinical backgrounds will be invited.While the overarching goal is to bridge the gap between mathematical modelling and clinical problems in general, the program has these specific aims:...
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